AI Model Evaluation

Evaluating a Large Language Model (LLM) performance and LLM training requires a structured approach, capturing the multi-dimensional nature of language generation. Lionbridge’s AI data solutions include AI model evaluation.

This AI data service relies on evaluation categories to comprehensively assess for language generation’s multi-dimensional nature. The categories ensure a thorough review of model outputs across quality, reliability, and user relevance. They also assure evaluations beyond surface-level correctness. Organizations can confidently assess whether a model’s outputs meet users’ expectations, align with business objectives, and adhere to ethical standards.

• Accuracy: Measures if the response is factually correct and free of errors.

• Fluency: Evaluates the grammatical correctness and natural language flow.

• Terminology: Assesses the correct usage of domain-specific terms and jargon.

• Readability: Measures how easily text is read and understood by the target audience.

• Cultural Relevance: Evaluates if the response is sensitive to, and appropriate for, the target audience’s cultural norms.

• Completeness: Checks whether all parts of the question are fully addressed.

• Relevance: Assesses if the response stays on topic and directly answers the query.

• Consistency: Ensures the response is internally logical and contradiction-free.

• Hallucination (inverted): Measures whether the model generates false information. (The fewer the hallucinations, the higher the inverted score.)

Read the brochure to learn more.

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Want to explore how we can help you get the most ROI from your AI model? Our data enrichment services include customized offerings like AI data labelling, data annotation, and custom dataset creation to help ensure your model is trained on high-quality labelled data. Let’s get in touch today.